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Found 2 papers, 1 papers with code
ScatterUQ: Interactive Uncertainty Visualizations for Multiclass Deep Learning Problems
ScatterUQ leverages recent advances in distance-aware neural networks, together with dimensionality reduction techniques, to construct robust, 2-D scatter plots explaining why a model predicts a test example to be (1) in-distribution and of a particular class, (2) in-distribution but unsure of the class, and (3) out-of-distribution.
Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification
We also present an ML framework that is designed to rapidly train with modest data requirements and provide both calibrated, predictive probabilities as well as an interpretable "out-of-distribution" (OOD) score to flag novel traffic samples.
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